Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f5ca2f7f048>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f5ca2e7a128>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    inputs = tf.placeholder(tf.float32, shape=[None, image_width, image_height, image_channels], name='inputs')
    z_data = tf.placeholder(tf.float32, shape=[None, z_dim], name='z_data')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')

    return inputs, z_data, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    
    # leaky ReLu alpha parameter
    alpha=0.2
    keep_prob = 0.7
    
    with tf.variable_scope('discriminator', reuse=reuse):
        # input image is 28x28x3 or 28x28x1
        x = tf.layers.conv2d(images, 64, 5, strides=2, padding='same', activation=None)
        x = tf.maximum(alpha*x,x)
        x = tf.nn.dropout(x,keep_prob)
        
        # 14x14x32
        x = tf.layers.conv2d(x, 128, 5, strides=2, padding='same', activation=None)
        x = tf.layers.batch_normalization(x, training=True)
        x = tf.maximum(alpha*x,x)
        x = tf.nn.dropout(x,keep_prob)

        # 7x7x128
        # resize to 8x8x128
        #x = tf.image.resize_images(x, size=[8,8])
        
        # 8x8x128
        x = tf.layers.conv2d(x, 256, 3, strides=2, padding='same', activation=None)
        # Do batch normalization only 1 time
        #x = tf.layers.batch_normalization(x, training=True)
        x = tf.maximum(alpha*x,x)

        # 4x4x256
        x = tf.reshape(x, (-1, 4*4*256))
        logits = tf.layers.dense(x, 1, activation=None)
       
        out = tf.sigmoid(logits)
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    # leaky ReLu alpha parameter
    alpha=0.2
    keep_prob = 0.5
    
    with tf.variable_scope('generator', reuse= not is_train):
        # First fully connected layer
        x = tf.layers.dense(z, 4*4*256, activation=None)
        x = tf.reshape(x, (-1, 4,4,256))
        x = tf.layers.batch_normalization(x, training=is_train)
        x = tf.maximum(alpha*x,x)
        x = tf.nn.dropout(x,keep_prob)
        
        # transpose convolution
        x = tf.layers.conv2d_transpose(x, 128, 3, strides=2, padding='same')
        x = tf.layers.batch_normalization(x, training=is_train)
        x = tf.maximum(alpha*x,x)
        x = tf.nn.dropout(x,keep_prob)
        
        # 8x8x128

        x = tf.layers.conv2d_transpose(x, 64, 3, strides=2, padding='same')
        x = tf.layers.batch_normalization(x, training=is_train)
        x = tf.maximum(alpha*x,x)
        
        # 16x16x128
        
        # Output layer, 32x32x out_channel_dim
        x = tf.layers.conv2d_transpose(x, out_channel_dim, 5, strides=2, padding='same')

        # resize to 28x28
        logits = tf.image.resize_images(x, size=[28,28])
        
        out = tf.tanh(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim, True)
    d_model_real, d_logits_real = discriminator(input_real, False)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*0.9))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [12]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
  
    image_channels = 1
    if (data_image_mode == 'RGB'):
        image_channels = 3
    
    inputs, z_data, learn_rate = model_inputs(data_shape[1], data_shape[2], image_channels, z_dim)
    learn_rate = learning_rate
        
    d_loss, g_loss = model_loss(inputs, z_data, image_channels)
        
    d_opt, g_opt = model_opt(d_loss, g_loss, learn_rate, beta1)
    
    steps = 0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                
                # Sample random noise for Generator
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # rescale batch image from -0.5,0.5 to -1,1
                batch_images *= 2

                # Run optimizers
                _ = sess.run(d_opt, feed_dict={inputs: batch_images, z_data: batch_z})
                _ = sess.run(g_opt, feed_dict={z_data: batch_z, inputs: batch_images})
                # run generator optimization 2 times to make sure that the discriminator loss does not go to zero
                _ = sess.run(g_opt, feed_dict={z_data: batch_z, inputs: batch_images})

                
                if steps % 50 == 0:
                    train_loss_d = d_loss.eval({inputs: batch_images, z_data: batch_z})
                    train_loss_g = g_loss.eval({z_data: batch_z})
                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    
                    show_generator_output(sess, 16, z_data, image_channels, data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [ ]:
batch_size = 64
z_dim = 200
learning_rate = 0.001
beta1 = 0.01


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 1.7806... Generator Loss: 1.7566
Epoch 1/2... Discriminator Loss: 1.4924... Generator Loss: 1.2209
Epoch 1/2... Discriminator Loss: 1.3501... Generator Loss: 1.2992
Epoch 1/2... Discriminator Loss: 1.2503... Generator Loss: 0.7707
Epoch 1/2... Discriminator Loss: 1.0929... Generator Loss: 0.9306
Epoch 1/2... Discriminator Loss: 1.4142... Generator Loss: 0.5658
Epoch 1/2... Discriminator Loss: 1.2145... Generator Loss: 0.8217
Epoch 1/2... Discriminator Loss: 1.1151... Generator Loss: 0.8941
Epoch 1/2... Discriminator Loss: 1.1938... Generator Loss: 1.4519
Epoch 1/2... Discriminator Loss: 1.1697... Generator Loss: 1.4807
Epoch 1/2... Discriminator Loss: 1.4599... Generator Loss: 0.5219
Epoch 1/2... Discriminator Loss: 1.1141... Generator Loss: 0.9398
Epoch 1/2... Discriminator Loss: 1.2067... Generator Loss: 0.8592
Epoch 1/2... Discriminator Loss: 1.1720... Generator Loss: 1.0873
Epoch 1/2... Discriminator Loss: 1.1197... Generator Loss: 0.9439
Epoch 1/2... Discriminator Loss: 1.1367... Generator Loss: 1.2999
Epoch 1/2... Discriminator Loss: 1.0888... Generator Loss: 1.1579
Epoch 1/2... Discriminator Loss: 1.1464... Generator Loss: 1.0007
Epoch 2/2... Discriminator Loss: 1.1763... Generator Loss: 0.9350
Epoch 2/2... Discriminator Loss: 1.1326... Generator Loss: 0.8369
Epoch 2/2... Discriminator Loss: 1.2665... Generator Loss: 0.8614
Epoch 2/2... Discriminator Loss: 1.2052... Generator Loss: 0.6970
Epoch 2/2... Discriminator Loss: 1.0875... Generator Loss: 1.1461
Epoch 2/2... Discriminator Loss: 1.0571... Generator Loss: 1.2639
Epoch 2/2... Discriminator Loss: 1.2526... Generator Loss: 0.6951
Epoch 2/2... Discriminator Loss: 1.1403... Generator Loss: 0.9903
Epoch 2/2... Discriminator Loss: 0.9671... Generator Loss: 1.5497
Epoch 2/2... Discriminator Loss: 1.4319... Generator Loss: 2.1734
Epoch 2/2... Discriminator Loss: 0.9762... Generator Loss: 1.5715
Epoch 2/2... Discriminator Loss: 1.0884... Generator Loss: 2.0877
Epoch 2/2... Discriminator Loss: 1.0367... Generator Loss: 1.0455
Epoch 2/2... Discriminator Loss: 0.9000... Generator Loss: 1.4916
Epoch 2/2... Discriminator Loss: 0.8858... Generator Loss: 1.6314
Epoch 2/2... Discriminator Loss: 0.9523... Generator Loss: 1.3007
Epoch 2/2... Discriminator Loss: 0.9822... Generator Loss: 1.3439
Epoch 2/2... Discriminator Loss: 1.1502... Generator Loss: 2.0814
Epoch 2/2... Discriminator Loss: 0.8727... Generator Loss: 1.2645

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [ ]:
batch_size = 64
z_dim = 200
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 1.1177... Generator Loss: 1.0527
Epoch 1/1... Discriminator Loss: 1.3958... Generator Loss: 0.8611
Epoch 1/1... Discriminator Loss: 1.5455... Generator Loss: 0.8034
Epoch 1/1... Discriminator Loss: 1.2867... Generator Loss: 0.9051
Epoch 1/1... Discriminator Loss: 1.2933... Generator Loss: 0.8839
Epoch 1/1... Discriminator Loss: 1.3787... Generator Loss: 0.7890
Epoch 1/1... Discriminator Loss: 1.3917... Generator Loss: 0.7082
Epoch 1/1... Discriminator Loss: 1.1631... Generator Loss: 1.1172
Epoch 1/1... Discriminator Loss: 1.2511... Generator Loss: 1.0258
Epoch 1/1... Discriminator Loss: 1.2914... Generator Loss: 1.0718
Epoch 1/1... Discriminator Loss: 1.3551... Generator Loss: 0.8493
Epoch 1/1... Discriminator Loss: 1.2288... Generator Loss: 0.9560
Epoch 1/1... Discriminator Loss: 1.3932... Generator Loss: 0.7126
Epoch 1/1... Discriminator Loss: 1.5227... Generator Loss: 0.6094
Epoch 1/1... Discriminator Loss: 1.2418... Generator Loss: 1.0711
Epoch 1/1... Discriminator Loss: 1.1629... Generator Loss: 0.8602
Epoch 1/1... Discriminator Loss: 1.2555... Generator Loss: 0.8586
Epoch 1/1... Discriminator Loss: 1.2848... Generator Loss: 0.8561
Epoch 1/1... Discriminator Loss: 1.3158... Generator Loss: 0.7290
Epoch 1/1... Discriminator Loss: 1.6072... Generator Loss: 0.4748
Epoch 1/1... Discriminator Loss: 1.2807... Generator Loss: 0.8594
Epoch 1/1... Discriminator Loss: 1.3597... Generator Loss: 0.9348
Epoch 1/1... Discriminator Loss: 1.2682... Generator Loss: 1.1756
Epoch 1/1... Discriminator Loss: 1.3675... Generator Loss: 0.7841
Epoch 1/1... Discriminator Loss: 1.3093... Generator Loss: 1.3657
Epoch 1/1... Discriminator Loss: 1.2611... Generator Loss: 0.7388
Epoch 1/1... Discriminator Loss: 1.4215... Generator Loss: 0.9664
Epoch 1/1... Discriminator Loss: 1.1867... Generator Loss: 0.8723
Epoch 1/1... Discriminator Loss: 1.2872... Generator Loss: 1.0179
Epoch 1/1... Discriminator Loss: 1.2096... Generator Loss: 0.7551
Epoch 1/1... Discriminator Loss: 1.3085... Generator Loss: 0.8676
Epoch 1/1... Discriminator Loss: 1.1319... Generator Loss: 1.0590
Epoch 1/1... Discriminator Loss: 1.2067... Generator Loss: 0.9762
Epoch 1/1... Discriminator Loss: 1.2340... Generator Loss: 1.1225
Epoch 1/1... Discriminator Loss: 1.1053... Generator Loss: 1.2259
Epoch 1/1... Discriminator Loss: 1.3022... Generator Loss: 1.3221
Epoch 1/1... Discriminator Loss: 1.2798... Generator Loss: 1.3826
Epoch 1/1... Discriminator Loss: 1.1948... Generator Loss: 0.7904
Epoch 1/1... Discriminator Loss: 1.4396... Generator Loss: 1.8389
Epoch 1/1... Discriminator Loss: 1.1304... Generator Loss: 0.9159
Epoch 1/1... Discriminator Loss: 1.1560... Generator Loss: 1.0643
Epoch 1/1... Discriminator Loss: 1.1566... Generator Loss: 0.8451
Epoch 1/1... Discriminator Loss: 1.2036... Generator Loss: 0.9836
Epoch 1/1... Discriminator Loss: 1.1688... Generator Loss: 1.0861
Epoch 1/1... Discriminator Loss: 1.3982... Generator Loss: 0.6285
Epoch 1/1... Discriminator Loss: 1.1577... Generator Loss: 0.9233
Epoch 1/1... Discriminator Loss: 1.2436... Generator Loss: 1.2984
Epoch 1/1... Discriminator Loss: 1.2506... Generator Loss: 0.8064
Epoch 1/1... Discriminator Loss: 1.1114... Generator Loss: 1.3729
Epoch 1/1... Discriminator Loss: 1.2814... Generator Loss: 1.6359
Epoch 1/1... Discriminator Loss: 1.3438... Generator Loss: 0.9178
Epoch 1/1... Discriminator Loss: 1.1646... Generator Loss: 1.1008
Epoch 1/1... Discriminator Loss: 1.1261... Generator Loss: 1.7767

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.